08-09-2014, 04:33 PM
Integer Wavelet Transform Based Steganographic Method Using Opa Algorithm Project Report
Integer Wavelet Transform.pdf (Size: 303.09 KB / Downloads: 94)
Abstract
This paper deals with secret communication in open environment like internet. Steganography
attempts to hide the secret information and make communication undetectable. Steganography is used to
conceal the secret information so that no one can sense the information.Steganographic method has many
challenges such as high hiding capacity and imperceptibility. In existing paper have some problems like less
robust and low hiding capacity. so in this paper I use integer wavelet transform(IWT) for increasing hiding
capacity and Optimum pixel adjustment algorithm(OPA) for enhancing the image quality use MATLAB to
implement my paper, because which has many inbuilt functions and easy to use.
Introduction
Steganography is the art and science of writing hidden messages in such a way that no one, apart from
the sender and intended recipient, suspects the existence of the message, a form of security through obscurity.
The word Steganography is of Greek origin and means "concealed writing" from the Greek words steganos
meaning "covered or protected", and graphic meaning "writing". The first recorded use of the term was in
1499.The advantage of Steganography, over cryptography alone, is that message do not attract attention to
themselves. Plainly visible encrypted messages no matter how unbreakable will arouse suspicion, and may in
themselves be incriminating in countries where encryption is illegal. Therefore, whereas cryptography protects
the contents of a message, Steganography can be said to protect both messages and communicating parties. It
includes the concealment of information within computer files .In digital Steganography, electronic
communications may include steganographic coding inside of a transport layer, such as a document file, image
file, program or protocol. Media files are ideal for steganographic transmission because of their large size. The
Least Significant Bit (LSB) substitution is an example of spatial domain techniques. The basic idea in LSB is
the direct replacement of LSBs of noisy or unused bits of the cover image with the secret message bits. Till now
LSB is the most preferred technique used for data hiding because it is simple to implement offers high hiding
capacity, and provides a very easy way to control stego-image quality [1] but it has low robustness to
modifications made to the stego-image such as low pass filtering and compression and also low imperceptibility.
Algorithms using LSB in greyscale images can be found in [2, 3, 4].
Integer Wavelet Transform
Generally wavelet domain allows us to hide data in regions that the human visual system (HVS) is less
sensitive to, such as the high resolution detail bands (HL, LH and HH), Hiding data in these regions allow us to
increase the robustness while maintaining good visual quality. Integer wavelet transform maps an integer data
set into another integer data set. In discrete wavelet transform, the used wavelet filters have floating point
coefficients so that when we hide data in their coefficients any truncations of the floating point values of the
pixels that should be integers may cause the loss of the hidden information which may lead to the failure of the
data hiding system [9]. To avoid problems of floating point precision of the wavelet filters when the input data
is integer as in digital images, the output data will no longer be integer which doesn't allow perfect
reconstruction of the input image [10] and in this case there will be no loss of information through forward and
inverse transform [9]. Due to the mentioned difference between integer wavelet transform (IWT) and discrete
wavelet transform (DWT) the LL sub band in the case of IWT appears to be a close copy with smaller scale of
the original image while in the case of DWT the resulting LL sub band is distorted. Lifting schemes is one of
many techniques that can be used to perform integer wavelet transform it is also the scheme used in this paper.
The following is an example showing how we can use lifting schemes to obtain integer wavelet transform by
using simple truncation and without losing inevitability.
The Extraction Algorithm
MATLAB is a numerical computing environment and fourth-generation programming language.
Developed by Math Works, MATLAB allows matrix manipulations, plotting of functions and data,
implementation of algorithms, creation of user interfaces, and interfacing with programs written in other
languages, including C, C++, Java, and Fortran. Although MATLAB is intended primarily for numerical
computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing
capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design
for dynamic and embedded systems. At the receiver uses the extraction algorithm to obtain the secret message.
The block diagram of the extraction algorithm is shown
Conclusions
In this paper i proposed a data hiding scheme that hides data into the integer wavelet coefficients of an
image. The system combines a data hiding technique and the optimum pixel adjustment algorithm to increase
the hiding capacity of the system compared to other systems. The proposed system hide secret data in a random
order using a secret key only known to both sender and receiver. In this method, embeds different number of
bits in each wavelet co efficicient according to a hiding capacity function in order to increasing the hiding
capacity without losses of the visual quality of resulting stego image. The proposed system also minimizes the
error difference between original coefficients values and modified values by using the optimum pixel
adjustment algorithm.